Share This Author
Item-based collaborative filtering recommendation algorithms
This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Analysis of recommendation algorithms for e-commerce
- B. Sarwar, G. Karypis, J. Konstan, J. Riedl
- Computer ScienceACM Conference on Economics and Computation
- 17 October 2000
This paper investigates several te hniques for analyzing large-s ale pur hase and preferen e data for the purpose of producing useful re ommendations to ustomers and devise and apply their ombinations on the authors' data sets to ompare for re Ommendation quality and performan e.
Application of Dimensionality Reduction in Recommender System - A Case Study
This paper presents two different experiments where one technology called Singular Value Decomposition (SVD) is explored to reduce the dimensionality of recommender system databases and suggests that SVD has the potential to meet many of the challenges ofRecommender systems, under certain conditions.
Combining Collaborative Filtering with Personal Agents for Better Recommendations
This paper shows that a CF framework can be used to combine personal IF agents and the opinions of a community of users to produce better recommendations than either agents or users can produce alone.
Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems
This paper proposes and experimentally validate a technique that has the potential to incrementally build SVD-based models and promises to make the recommender systems highly scalable.
Application of Dimensionality Reduction in Recommender Systems
Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system
- B. Sarwar, J. Konstan, Al Borchers, Jonathan L. Herlocker, Bradley N. Miller, J. Riedl
- Computer ScienceConference on Computer Supported Cooperative Work
- 1 November 1998
The filterbot model allows collaborative filtering systems to address sparsity by tapping the strength of content filtering techniques and is experimentally validated by showing that even simple filterbots such as spell checking can increase the utility for users of sparsely populated collaborative filtering system.
Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering
This work addresses the performance issues of recommender system technologies by scaling up the neighborhood formation process through the use of clustering techniques.
Large-scale item categorization for e-commerce
This paper demonstrates through extensive experimental evaluation that the proposed hierarchical approach is superior to flat models, and the data-driven extraction of latent groups works significantly better than the existing human-defined hierarchy.
Sparsity, scalability, and distribution in recommender systems
This dissertation presents the approaches to address three research challenges—sparsity, scalability, and distribution, and presents a taxonomy of recommender system applications based on the relative relevance of geographically proximate and distant users and items.